---
title: Transforms examples
description: These examples demonstrate how to use transforms to derive useful insights from your data. All the examples use one of the Kibana sample datasets. For...
url: https://www.elastic.co/elastic/docs-builder/docs/3028/explore-analyze/transforms/transform-examples
products:
- Elasticsearch
applies_to:
- Elastic Cloud Serverless: Generally available
- Elastic Stack: Generally available
---
# Transforms examples
These examples demonstrate how to use transforms to derive useful insights from your data. All the examples use one of the [Kibana sample datasets](https://www.elastic.co/elastic/docs-builder/docs/3028/explore-analyze). For a more detailed, step-by-step example, see [Tutorial: Transforming the eCommerce sample data](https://www.elastic.co/elastic/docs-builder/docs/3028/explore-analyze/transforms/ecommerce-transforms).
- [Finding your best customers](#example-best-customers)
- [Finding air carriers with the most delays](#example-airline)
- [Finding suspicious client IPs](#example-clientips)
- [Finding the last log event for each IP address](#example-last-log)
- [Finding client IPs that sent the most bytes to the server](#example-bytes)
- [Getting customer name and email address by customer ID](#example-customer-names)
## Finding your best customers
This example uses the eCommerce orders sample data set to find the customers who spent the most in a hypothetical webshop. Let’s use the `pivot` type of transform such that the destination index contains the number of orders, the total price of the orders, the amount of unique products and the average price per order, and the total amount of ordered products for each customer.
Alternatively, you can use the [preview transform](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-transform-preview-transform) and the [create transform API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-transform-put-transform).
```json
{
"source": {
"index": "kibana_sample_data_ecommerce"
},
"dest" : { <1>
"index" : "sample_ecommerce_orders_by_customer"
},
"pivot": {
"group_by": { <2>
"user": { "terms": { "field": "user" }},
"customer_id": { "terms": { "field": "customer_id" }}
},
"aggregations": {
"order_count": { "value_count": { "field": "order_id" }},
"total_order_amt": { "sum": { "field": "taxful_total_price" }},
"avg_amt_per_order": { "avg": { "field": "taxful_total_price" }},
"avg_unique_products_per_order": { "avg": { "field": "total_unique_products" }},
"total_unique_products": { "cardinality": { "field": "products.product_id" }}
}
}
}
```
In the example above, condensed JSON formatting is used for easier readability of the pivot object.
The preview transforms API enables you to see the layout of the transform in advance, populated with some sample values. For example:
```js
{
"preview" : [
{
"total_order_amt" : 3946.9765625,
"order_count" : 59.0,
"total_unique_products" : 116.0,
"avg_unique_products_per_order" : 2.0,
"customer_id" : "10",
"user" : "recip",
"avg_amt_per_order" : 66.89790783898304
},
...
]
}
```
This transform makes it easier to answer questions such as:
- Which customers spend the most?
- Which customers spend the most per order?
- Which customers order most often?
- Which customers ordered the least number of different products?
It’s possible to answer these questions using aggregations alone, however transforms allow us to persist this data as a customer centric index. This enables us to analyze data at scale and gives more flexibility to explore and navigate data from a customer centric perspective. In some cases, it can even make creating visualizations much simpler.
## Finding air carriers with the most delays
This example uses the Flights sample data set to find out which air carrier had the most delays. First, filter the source data such that it excludes all the cancelled flights by using a query filter. Then transform the data to contain the distinct number of flights, the sum of delayed minutes, and the sum of the flight minutes by air carrier. Finally, use a [`bucket_script`](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/3028/reference/aggregations/search-aggregations-pipeline-bucket-script-aggregation) to determine what percentage of the flight time was actually delay.
```json
{
"source": {
"index": "kibana_sample_data_flights",
"query": { <1>
"bool": {
"filter": [
{ "term": { "Cancelled": false } }
]
}
}
},
"dest" : { <2>
"index" : "sample_flight_delays_by_carrier"
},
"pivot": {
"group_by": { <3>
"carrier": { "terms": { "field": "Carrier" }}
},
"aggregations": {
"flights_count": { "value_count": { "field": "FlightNum" }},
"delay_mins_total": { "sum": { "field": "FlightDelayMin" }},
"flight_mins_total": { "sum": { "field": "FlightTimeMin" }},
"delay_time_percentage": { <4>
"bucket_script": {
"buckets_path": {
"delay_time": "delay_mins_total.value",
"flight_time": "flight_mins_total.value"
},
"script": "(params.delay_time / params.flight_time) * 100"
}
}
}
}
}
```
The preview shows you that the new index would contain data like this for each carrier:
```js
{
"preview" : [
{
"carrier" : "ES-Air",
"flights_count" : 2802.0,
"flight_mins_total" : 1436927.5130677223,
"delay_time_percentage" : 9.335543983955839,
"delay_mins_total" : 134145.0
},
...
]
}
```
This transform makes it easier to answer questions such as:
- Which air carrier has the most delays as a percentage of flight time?
This data is fictional and does not reflect actual delays or flight stats for any of the featured destination or origin airports.
## Finding suspicious client IPs
This example uses the web log sample data set to identify suspicious client IPs. It transforms the data such that the new index contains the sum of bytes and the number of distinct URLs, agents, incoming requests by location, and geographic destinations for each client IP. It also uses filter aggregations to count the specific types of HTTP responses that each client IP receives. Ultimately, the example below transforms web log data into an entity centric index where the entity is `clientip`.
```json
{
"source": {
"index": "kibana_sample_data_logs"
},
"dest" : { <1>
"index" : "sample_weblogs_by_clientip"
},
"sync" : { <2>
"time": {
"field": "timestamp",
"delay": "60s"
}
},
"pivot": {
"group_by": { <3>
"clientip": { "terms": { "field": "clientip" } }
},
"aggregations": {
"url_dc": { "cardinality": { "field": "url.keyword" }},
"bytes_sum": { "sum": { "field": "bytes" }},
"geo.src_dc": { "cardinality": { "field": "geo.src" }},
"agent_dc": { "cardinality": { "field": "agent.keyword" }},
"geo.dest_dc": { "cardinality": { "field": "geo.dest" }},
"responses.total": { "value_count": { "field": "timestamp" }},
"success" : { <4>
"filter": {
"term": { "response" : "200"}}
},
"error404" : {
"filter": {
"term": { "response" : "404"}}
},
"error5xx" : {
"filter": {
"range": { "response" : { "gte": 500, "lt": 600}}}
},
"timestamp.min": { "min": { "field": "timestamp" }},
"timestamp.max": { "max": { "field": "timestamp" }},
"timestamp.duration_ms": { <5>
"bucket_script": {
"buckets_path": {
"min_time": "timestamp.min.value",
"max_time": "timestamp.max.value"
},
"script": "(params.max_time - params.min_time)"
}
}
}
}
}
```
After you create the transform, you must start it:
```json
```
Shortly thereafter, the first results should be available in the destination index:
```json
```
The search result shows you data like this for each client IP:
```js
"hits" : [
{
"_index" : "sample_weblogs_by_clientip",
"_id" : "MOeHH_cUL5urmartKj-b5UQAAAAAAAAA",
"_score" : 1.0,
"_source" : {
"geo" : {
"src_dc" : 2.0,
"dest_dc" : 2.0
},
"success" : 2,
"error404" : 0,
"error503" : 0,
"clientip" : "0.72.176.46",
"agent_dc" : 2.0,
"bytes_sum" : 4422.0,
"responses" : {
"total" : 2.0
},
"url_dc" : 2.0,
"timestamp" : {
"duration_ms" : 5.2191698E8,
"min" : "2020-03-16T07:51:57.333Z",
"max" : "2020-03-22T08:50:34.313Z"
}
}
}
]
```
Like other Kibana sample data sets, the web log sample dataset contains timestamps relative to when you installed it, including timestamps in the future. The continuous transform will pick up the data points once they are in the past. If you installed the web log sample dataset some time ago, you can uninstall and reinstall it and the timestamps will change.
This transform makes it easier to answer questions such as:
- Which client IPs are transferring the most amounts of data?
- Which client IPs are interacting with a high number of different URLs?
- Which client IPs have high error rates?
- Which client IPs are interacting with a high number of destination countries?
## Finding the last log event for each IP address
This example uses the web log sample data set to find the last log from an IP address. Let’s use the `latest` type of transform in continuous mode. It copies the most recent document for each unique key from the source index to the destination index and updates the destination index as new data comes into the source index.
Pick the `clientip` field as the unique key; the data is grouped by this field. Select `timestamp` as the date field that sorts the data chronologically. For continuous mode, specify a date field that is used to identify new documents, and an interval between checks for changes in the source index.
Let’s assume that we’re interested in retaining documents only for IP addresses that appeared recently in the log. You can define a retention policy and specify a date field that is used to calculate the age of a document. This example uses the same date field that is used to sort the data. Then set the maximum age of a document; documents that are older than the value you set will be removed from the destination index.
This transform creates the destination index that contains the latest login date for each client IP. As the transform runs in continuous mode, the destination index will be updated as new data that comes into the source index. Finally, every document that is older than 30 days will be removed from the destination index due to the applied retention policy.
```json
{
"source": {
"index": [
"kibana_sample_data_logs"
]
},
"latest": {
"unique_key": [ <1>
"clientip"
],
"sort": "timestamp" <2>
},
"frequency": "1m", <3>
"dest": {
"index": "last-log-from-clientip"
},
"sync": { <4>
"time": {
"field": "timestamp",
"delay": "60s"
}
},
"retention_policy": { <5>
"time": {
"field": "timestamp",
"max_age": "30d"
}
},
"settings": {
"max_page_search_size": 500
}
}
```
After you create the transform, start it:
```json
```
After the transform processes the data, search the destination index:
```json
```
The search result shows you data like this for each client IP:
```js
{
"_index" : "last-log-from-clientip",
"_id" : "MOeHH_cUL5urmartKj-b5UQAAAAAAAAA",
"_score" : 1.0,
"_source" : {
"referer" : "http://twitter.com/error/don-lind",
"request" : "/elasticsearch",
"agent" : "Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; .NET CLR 1.1.4322)",
"extension" : "",
"memory" : null,
"ip" : "0.72.176.46",
"index" : "kibana_sample_data_logs",
"message" : "0.72.176.46 - - [2018-09-18T06:31:00.572Z] \"GET /elasticsearch HTTP/1.1\" 200 7065 \"-\" \"Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; .NET CLR 1.1.4322)\"",
"url" : "https://www.elastic.co/downloads/elasticsearch",
"tags" : [
"success",
"info"
],
"geo" : {
"srcdest" : "IN:PH",
"src" : "IN",
"coordinates" : {
"lon" : -124.1127917,
"lat" : 40.80338889
},
"dest" : "PH"
},
"utc_time" : "2021-05-04T06:31:00.572Z",
"bytes" : 7065,
"machine" : {
"os" : "ios",
"ram" : 12884901888
},
"response" : 200,
"clientip" : "0.72.176.46",
"host" : "www.elastic.co",
"event" : {
"dataset" : "sample_web_logs"
},
"phpmemory" : null,
"timestamp" : "2021-05-04T06:31:00.572Z"
}
}
```
This transform makes it easier to answer questions such as:
- What was the most recent log event associated with a specific IP address?
## Finding client IPs that sent the most bytes to the server
This example uses the web log sample data set to find the client IP that sent the most bytes to the server in every hour. The example uses a `pivot` transform with a [`top_metrics`](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/3028/reference/aggregations/search-aggregations-metrics-top-metrics) aggregation.
Group the data by a [date histogram](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/3028/reference/aggregations/search-aggregations-bucket-composite-aggregation#_date_histogram) on the time field with an interval of one hour. Use a [max aggregation](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/3028/reference/aggregations/search-aggregations-metrics-max-aggregation) on the `bytes` field to get the maximum amount of data that is sent to the server. Without the `max` aggregation, the API call still returns the client IP that sent the most bytes, however, the amount of bytes that it sent is not returned. In the `top_metrics` property, specify `clientip` and `geo.src`, then sort them by the `bytes` field in descending order. The transform returns the client IP that sent the biggest amount of data and the 2-letter ISO code of the corresponding location.
```json
{
"source": {
"index": "kibana_sample_data_logs"
},
"pivot": {
"group_by": { <1>
"timestamp": {
"date_histogram": {
"field": "timestamp",
"fixed_interval": "1h"
}
}
},
"aggregations": {
"bytes.max": { <2>
"max": {
"field": "bytes"
}
},
"top": {
"top_metrics": { <3>
"metrics": [
{
"field": "clientip"
},
{
"field": "geo.src"
}
],
"sort": {
"bytes": "desc"
}
}
}
}
}
}
```
The API call above returns a response similar to this:
```js
{
"preview" : [
{
"top" : {
"clientip" : "223.87.60.27",
"geo.src" : "IN"
},
"bytes" : {
"max" : 6219
},
"timestamp" : "2021-04-25T00:00:00.000Z"
},
{
"top" : {
"clientip" : "99.74.118.237",
"geo.src" : "LK"
},
"bytes" : {
"max" : 14113
},
"timestamp" : "2021-04-25T03:00:00.000Z"
},
{
"top" : {
"clientip" : "218.148.135.12",
"geo.src" : "BR"
},
"bytes" : {
"max" : 4531
},
"timestamp" : "2021-04-25T04:00:00.000Z"
},
...
]
}
```
## Getting customer name and email address by customer ID
This example uses the ecommerce sample data set to create an entity-centric index based on customer ID, and to get the customer name and email address by using the `top_metrics` aggregation.
Group the data by `customer_id`, then add a `top_metrics` aggregation where the `metrics` are the `email`, the `customer_first_name.keyword`, and the `customer_last_name.keyword` fields. Sort the `top_metrics` by `order_date` in descending order. The API call looks like this:
```json
{
"source": {
"index": "kibana_sample_data_ecommerce"
},
"pivot": {
"group_by": { <1>
"customer_id": {
"terms": {
"field": "customer_id"
}
}
},
"aggregations": {
"last": {
"top_metrics": { <2>
"metrics": [
{
"field": "email"
},
{
"field": "customer_first_name.keyword"
},
{
"field": "customer_last_name.keyword"
}
],
"sort": {
"order_date": "desc"
}
}
}
}
}
}
```
The API returns a response that is similar to this:
```js
{
"preview" : [
{
"last" : {
"customer_last_name.keyword" : "Long",
"customer_first_name.keyword" : "Recip",
"email" : "recip@long-family.zzz"
},
"customer_id" : "10"
},
{
"last" : {
"customer_last_name.keyword" : "Jackson",
"customer_first_name.keyword" : "Fitzgerald",
"email" : "fitzgerald@jackson-family.zzz"
},
"customer_id" : "11"
},
{
"last" : {
"customer_last_name.keyword" : "Cross",
"customer_first_name.keyword" : "Brigitte",
"email" : "brigitte@cross-family.zzz"
},
"customer_id" : "12"
},
...
]
}
```